Revolutionize Your Data Workflow with Polars: The Ultimate Pandas Replacement
@YVictor
Introduction
Core Features and Advantages
Real-World Applications
Q&A
"10 Things I Hate About pandas" by Wes McKinney
Columnar
Standardization
Cross Languages
Diverse Data Type
Nested Data Type
https://www.pola.rs/posts/i-wrote-one-of-the-fastest-dataframe-libraries/
Support for lazy mode and query optimization.
Utilizes all available cores on your machine without any additional configuration.
Minimizes redundant data copying, saves memory.
Rust which gives it C/C++ performance and allows it to fully control performance critical parts in a query engine.
Apache Arrow is a cross-language, columnar memory format for big data analytics, enabling efficient data sharing between systems.
Enables custom computations and transformations for composable operations and better performance.
https://h2oai.github.io/db-benchmark/#join
https://h2oai.github.io/db-benchmark/#groupby
https://github.com/Yvictor/polars_ta_extension
yvictor3141@gmail.com
Polars Rust Python
By yvictor
